Replication data for: Multi-objective application placement in fog computing using graph neural network-based reinforcement learning
This dataset comprises a collection of synthetic application‐placement instance sets for heterogeneous cloud–edge/fog infrastructures, designed for the evaluation of single‐ and multi‐objective optimization strategies. Each instance describes: - a directed acyclic graph (DAG) of interdependent servi...
| Autores: | , |
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| Formato: | conjunto de datos |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Consorci de Serveis Universitaris de Catalunya (CSUC) |
| Repositorio: | CORA.Repositori de Dades de Recerca |
| OAI Identifier: | oai:dnet:cora.rdr____::19ee670348178df8f4650a0fddf51ab7 |
| Acesso em linha: | https://doi.org/10.34810/DATA2671 |
| Access Level: | acceso abierto |
| Palavra-chave: | Computer and Information Science Deep reinforcement learning multi-objective-optimization fog computing |
| Resumo: | This dataset comprises a collection of synthetic application‐placement instance sets for heterogeneous cloud–edge/fog infrastructures, designed for the evaluation of single‐ and multi‐objective optimization strategies. Each instance describes: - a directed acyclic graph (DAG) of interdependent services forming an application, - a set of compute nodes (cloud, edge, fog) with resource capacities and connectivity latencies, - resource demands of each service (e.g., CPU, memory), service‐to‐service dependency weights or communication cost, - one or more placement solutions together with objective values (such as latency, energy consumption, deployment cost) generated by algorithms including the DRL model, a genetic algorithm (GA) and an NSGA-II multi‐objective heuristic. The dataset is split into training and test sets and is generated via the provided instance_generator.py and generate_dataset.py scripts. It allows researchers to benchmark and compare placement algorithms in terms of Pareto-front coverage, convergence speed, and trade-offs between objectives. Potential uses: Investigating learning‐based or heuristic algorithms for application placement, multi‐objective optimisation in the cloud/fog continuum, dependency‐aware placement of microservices, as well as enabling reproducibility and comparison across approaches. |
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